System and method to incorporate node fulfillment capacity and capacity utilization in balancing fulfillment load across retail supply networks

ABSTRACT

Evaluating node fulfillment capacity in node order assignment by receiving a current order for node order assignment, retrieving data of each node, the retrieved data of each node including current capacity utilization, determining a probability of backlog on an expected ship date of each node, the probability of backlog being based on the retrieved current capacity utilization, determining a capacity utilization cost of each node based on the probability of backlog on the expected ship date, automatically calculating a fulfillment cost of each node of the current order based on the capacity utilization cost, identifying one or more nodes for the current order with the lowest fulfillment cost and automatically generating a node order assignment assigning the current order to one of the one or more nodes with the lowest fulfillment cost.

BACKGROUND

This disclosure is directed to computer generated node order fulfillmentperformance and more particularly, to computer generated node orderfulfillment performance considering capacity utilization cost.

Omni-channel retailers employ a number of channels to fulfill onlineorders. One approach to find optimal fulfillment solutions is to modelthe fulfillment problem as a multi-objective optimization problem, wherethe solution is order item assignments across a large number offulfillment candidate nodes (stores, ecommerce fulfillment centers,etc.).

A key issue when assigning a part of an order to a node for fulfillmentis that the order can get backlogged due to limited node capacity, thatis, the laborers who can pick the items at the node and fulfill theorder. Node capacity is especially a problem when non-traditionalfulfillment nodes are considered in the node fulfillment decision suchas stores in the recent ship-from-store trend. On the other hand, nodescan remain underutilized—having more capacity available than is beingused. Therefore, factoring in the node fulfillment capacity and capacityutilization of a node would be useful for balancing fulfillment loadacross retail supply networks and avoiding costly delays due tooverloading the current resources of the node.

SUMMARY OF THE INVENTION

One embodiment is directed to a method for evaluating node fulfillmentcapacity in node order assignment. The method includes receiving acurrent order for node order assignment, retrieving data of each nodefrom a plurality of nodes, the retrieved data of each node comprisingcurrent capacity utilization, determining a probability of backlog on anexpected ship date of each node of the plurality of nodes, theprobability of backlog being based on the retrieved current capacityutilization, determining a capacity utilization cost of each node of theplurality of nodes based on the probability of backlog on the expectedship date, automatically calculating a fulfillment cost of each node ofthe plurality of nodes of the current order based on the capacityutilization cost, identifying one or more nodes from the plurality ofnodes of the current order with the lowest fulfillment cost andautomatically generating a node order assignment assigning the currentorder to one of the one or more nodes of the plurality of nodes with thelowest fulfillment cost.

A system that includes one or more processors operable to perform one ormore methods described herein also may be provided.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

These are other objects, features and advantages of the presentinvention will become apparent from the following detailed description,which is to be read in connection with the accompanying drawing, inwhich:

FIG. 1 is a flow chart of the steps of one embodiment of the method ofthe invention.

FIG. 2 is a graph of determining the probability of backlog based onhistorical data of current capacity utilization.

FIG. 3 is a graph of determining the probability of backlog based onretrieved data of current capacity utilization and hours left in acurrent day.

FIG. 4 is a block diagram of one embodiment of the system of theinvention.

FIG. 5 is a graph of the calculation of a capacity utilization cost inthe system of the invention.

FIG. 6 is a block diagram of one embodiment of the computation engine ofthe invention.

FIG. 7 is a block diagram of one embodiment of the integration betweenthe computation engine of the invention with a fulfillment engine andother cost computation engines.

FIG. 8 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 9 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 10 is a block diagram of an exemplary computing system suitable forimplementation of this invention.

DETAILED DESCRIPTION

This invention is a system and method for evaluating node fulfillmentcapacity in node order assignment. This invention incorporates nodefulfillment capacity and capacity utilization into a calculation offulfillment cost in a fulfillment engine, for example, into amulti-objective optimization-based engine to factor in when balancing anumber of objectives for node order assignment. The invention derives amathematical modeling approach enabling the incorporation of nodefulfillment capacity and capacity utilization into a fulfillment engine,and a methodology for converting the raw retail data into a calculablefulfillment capacity utilization cost. This invention enables providinga real dollar cost for load balancing based, on futurepredicted/expected cost, and thus enables trading off with other orderfulfillment objectives that have real dollar values such as shippingcost.

Node fulfillment capacity and capacity utilization are modeled in such away that in general, the node with lower cumulative capacity utilizationis preferred, or, given equal current capacity consumption, the nodewith higher capacity is preferred. The weight applied to the calculatedfulfillment capacity utilization cost can be adjusted to trade-offbetween improving fulfillment capacity utilization with other businessobjectives.

By factoring in node fulfillment capacity and capacity utilization inthe fulfillment decision, the retailers are able to balance the workloadbetween fulfillment nodes with their actual, real-time capacity andcapacity utilization; reduce labor cost from extra workers hired fororder fulfillment at a node beyond its capacity or else avoid futuredelaying or upgrading of orders due to failure of an over-loaded node toprocess (pick, pack, and ship) all orders on time.

As is shown in FIG. 1, one embodiment of the method of the inventionbegins with step S100 of receiving a current order for node orderassignment. At step S102, the system retrieves data of each node from aplurality of nodes, the retrieved data of each node comprising currentcapacity utilization. Current capacity utilization is the percentage oftotal daily capacity used so far in a current day, without takingbacklog into consideration, and is calculated from the number of unitsassigned for processing so far in a current day divided by the capacityof that current day. Current capacity utilization is updatedregularly-for example, based on a predetermined time interval or realtime. Continuous order assignment to a node increases capacityutilization of that node.

Then, at step S104, the system determines a probability of backlog on anexpected ship date of each node of the plurality of nodes, theprobability of backlog being based on the retrieved current capacityutilization. The expected ship date of a node is the date on which thecurrent order is expected to be shipped from that node. The probabilityof backlog on the expected ship date is the probability that the currentorder will be backlogged on that expected ship date due to lack ofcapacity given the current capacity utilization level.

Further at step S106, the system automatically converts the probabilityof backlog, backlog cost, and labor cost of each node of the pluralityof nodes into a capacity utilization cost of the each node using acapacity utilization cost model defining a set of predetermined capacityutilization threshold values. Backlog cost is the average cost per unitassociated with expediting backlogged order. Backlog cost can increasefor each day an order is waiting to be shipped and can also take intoaccount the shipping cost of an order. Labor cost is the cost of laborper unit pick up. The set of predetermined capacity utilizationthreshold values can be set by the retailers according to their ownobjective weighting. One example of determining the set of the capacityutilization threshold values is to evenly break up the capacityutilization into a set of threshold values having equal intervals.

At step S108, the system automatically calculates a fulfillment cost ofeach node of the plurality of nodes of the current order by adding aplurality of costs. In one embodiment, the plurality of costs includesshipping cost and the capacity utilization cost. At step S110, thesystem further identifies one or more nodes from the plurality of nodesof the current order with the lowest fulfillment cost. Finally, at stepS112, the system automatically generates a node order assignmentassigning the current order to one of the one or more nodes with thelowest fulfillment cost.

In one embodiment, the retrieved data of each node further comprisesbacklog data, capacity of a current day, and capacity of a future day.Backlog data is the backlog units at the beginning of the current day.Capacity of a current day is a planned capacity in units of the currentday. Capacity of a future day is a planned capacity in units of a day inthe future of the current day. In another embodiment, the system furtherautomatically calculates an actual capacity utilization on the expectedship date of each node of the plurality of nodes, the actual capacityutilization being based on the retrieved current capacity utilization,capacity of a current day, the capacity of a future day, and thebacklog. The actual capacity utilization differs from the currentcapacity utilization by taking backlog into consideration.

In one embodiment, when there is enough capacity for the current day,the actual capacity utilization is calculated by adding the result ofthe backlog divided by the capacity of a current day to the currentcapacity utilization. In another embodiment, when there is not enoughcapacity for the current day, the actual capacity utilization iscalculated by the backlog on an expected ship date divided by thecapacity of that day, where the backlog on an expected ship date iscalculated by adding the result of the current capacity utilizationmultiplied by the capacity of a current day to the backlog andsubtracting the result of the capacity of each day before the expectedship date multiplied by the backlog days of the current order before theexpected ship date.

In one embodiment, the probability of backlog is calculated byhistorical data of backlogged orders at the current capacity utilizationdivided by historical data of total orders at the current capacityutilization. One example of determining the probability of backlog isshown in FIG. 2. Given a node was assigned to an order, the systemcomputes the current capacity utilization of that node at the assignmenttime and records whether the order eventually became backlogged (couldnot be processed on the same day that the order was assigned to thatnode). From historical data, the system considers order status of a day(1 if the order was backlogged, 0 if the order was not backlogged) as aprobabilistic function based on current capacity utilization at the timeof order sourcing. The system then divides the current capacityutilization into region 1, region 2 . . . region m, which can becustomized by retailers. For a given interval of current capacityutilization in one of the divided regions, the system computes theprobability of backlog from the number of backlogged orders in thehistorical data at the interval divided by the total orders recorded inthe historical data at that interval.

The probability of backlog can be direct counts in each utilizationregion or an actual continuous, fitted probabilistic model, such as alogistic regression model, a kernel regression, or similar model. Oneexample of fitting of probabilistic model is shown as the sigmoid 4 inFIG. 2, which gives retailers choice to set up a threshold value abovewhich probability of backlog is significant. In this example, theretailer can avoid node assignment for nodes having a probability ofbacklog value above the significant threshold. For both direct count andcontinuous model approach, probability of backlog as a function ofutilization can be incorporated through techniques such as addingconstraints in the modeling or using Bayesian priors.

In another embodiment, the probability of backlog is further based onhours left in the current day. FIG. 3 is one example of plot ofhistorical data for computing the probability of backlog based on thecurrent capacity utilization and the hours left in the current day.Given a node was assigned to an order, the system computes the currentcapacity utilization of that node and the hours left in the current dayof that node both at the time the order is assigned. The system thenrecords whether the order eventually became backlogged (could not beprocessed on the same day that the order was assigned to that node).From historical data, the system considers order status (whether anorder becomes backlogged) as a probabilistic function based on thecurrent capacity utilization at the time of order sourcing and the hoursleft in the current day. The system then divides both the currentcapacity utilization and the hours left in the current day intodifferent intervals. For a given interval restricted by the currentcapacity utilization and the hours left in the current day, the systemcomputes the probability of backlog from the number of backlogged ordersin the historical data divided by the total orders recorded in thehistorical data at that interval.

The probability of backlog can be direct counts in each utilizationregion or an actual continuous, fitted probabilistic model, such as alogistic regression model, a kernel regression, or similar model. Oneexample of fitting of probabilistic model is shown as the sigmoid 6 inFIG. 3, which gives retailers choice to set up a threshold value abovewhich probability of backlog is significant. In this example, theretailer can avoid node assignment for nodes having a probability ofbacklog value above the significant threshold. For both direct count andcontinuous model approach, probability of backlog as a function ofutilization can be incorporated through techniques such as addingconstraints in the modeling or using Bayesian priors.

In one embodiment, the method further comprises determining a number ofdays of backlog on an expected ship date of each node of the pluralityof nodes. The number of days of backlog is based on the retrievedcurrent capacity utilization. The capacity utilization cost furtherconsiders the number of days of backlog. The probability of an orderhaving different possible number of days (0 days, 1 day, 2 days etc.) ofbacklog is retrieved from historical data. Upon assigning an order to anode, the order has a probability of being backlogged for 0 days, 1 day,2 days, etc. For each historical order, the final number of days thatorder was in backlog is taken along with the state of the node at thetime the order was assigned, including current capacity utilization,cumulative capacity utilization level (actual capacity utilization), aswell as other possible input features such as hours left in the day. Aprobabilistic model is designed that provides as output the number ofdays of backlog given the state of the node as input features The inputfeatures can be set to a count probability distribution such as thePoisson distribution the Negative Binomial distribution, or Gammadistribution. For example, in the Poisson distribution model, thevariables are predictors for the rate parameter of the distribution. ThePoisson distribution assigns a probability to each value 0, 1, throughinfinity—corresponding to the probability for 0, 1, etc. of the numberof days of backlog. The parameters for the probabilistic model arechosen such that they minimize the statistical risk given the observedhistorical data, or any other number of statistical inferencetechniques. Furthermore, a cost of backlog is associated with eachnumber of days (0 days, 1 day, 2 days etc.), reflecting the costincurred if the order were backlogged for that number of days. Finally,the capacity utilization cost is then determined as the expected costunder this per-day distribution and costs. For example, the probabilityof 0 day of backlog multiplied by the cost of 0 day of backlog, plus theprobability of 1 day of backlog multiplied by the cost of 1 day ofbacklog, and so on. The model then automatically sums up a total costincurred by the possible backlog days for rendering a capacityutilization cost.

FIG. 4 depicts one embodiment of a capacity utilization cost system 1.The capacity utilization cost model 11 takes current capacityutilization 12, backlog 14, capacity of a current day 16, capacity of afuture day 17 and hours left in a current day 18 to determine theprobability of backlog 20. The capacity utilization cost model 11 thentakes labor cost 22, backlog cost 24 and the determined probability ofbacklog 20 into consideration for automatically converting them into acapacity utilization cost 26.

FIG. 5 depicts one embodiment of the calculation and conversion ofcollected data into a capacity utilization cost inside the capacityutilization cost model. CU1, CU2, CU3 and CU4 are predetermined capacityutilization threshold values defined by the model. The predeterminedcapacity utilization threshold values can be customized by retailersbased on their own needs. The system determines a probability of backlogfrom retrieved current capacity utilization data that is corresponded inthe predetermined capacity utilization threshold values. The system thenconverts the probability of backlog, backlog cost, and labor cost into acapacity utilization cost.

As is shown in FIG. 6, the diagram depicts one embodiment of thefulfillment capacity utilization load balancing cost computation engine8. The fulfillment capacity utilization load balancing cost computationengine 8 considers the capacity of a current day and the capacity of afuture day from a planned daily capacity database 28, the number ofunits assigned from a node unit assignment database 30, backlog from abacklog database 32, backlog cost from a backlog cost database 34, laborcost from a labor cost database 36 and predetermined capacityutilization threshold values from a capacity utilization target rangedatabase 38 for converting the data considered into a capacityutilization cost.

As is shown in FIG. 7, the diagram depicts one embodiment of theintegration between the fulfillment capacity utilization load balancingcost computation engine 8 with a fulfillment engine 40, other costcomputation engine 42 and other cost computation engine 44. One exampleof the other cost computation engine 42 is a cancellation costcomputation engine, which takes a variety of data into consideration.The data includes order scheduled, order release status and labor cost.One example of the other cost computation engine 44 is a loyalty rewardmodule, which takes a variety of data into consideration. The dataincludes customer loyalty reward data and carrier and shipping methodspecific CO₂/miles data. The data considered by other cost computationengine 42 and other cost computation engine 44 can be overlapping. Thefulfillment engine 40 minimizes a fulfillment cost of an order applyingcustomer business objective weighting to costs calculated from thefulfillment capacity utilization load balancing cost computation engine8, other cost computation engine 42, other cost computation engine 44,and shipping matrix 51. The fulfillment engine 40 obtains inventory data46, zipcode-store-distance data 48 and store lists 52 for calculating afulfillment cost and identifying one or more nodes for order fulfillmentperformance. The system can be implemented as a cloud system or anon-premise system.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed. Cloud computing is a model of service delivery forenabling convenient, on-demand network access to a shared pool ofconfigurable computing resources (e.g., networks, network bandwidth,servers, processing, memory, storage, applications, virtual machines,and services) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service. Thiscloud model may include at least five characteristics, at least threeservice models, and at least four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 8, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 8 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 9, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 8) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 9 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and order fulfillment optimization 96.

FIG. 10 illustrates a schematic of an example computer or processingsystem that may implement the method for evaluating node fulfillmentcapacity in node order assignment. The computer system is only oneexample of a suitable processing system and is not intended to suggestany limitation as to the scope of use or functionality of embodiments ofthe methodology described herein. The processing system shown may beoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with the processing system shown in FIG. 10 mayinclude, but are not limited to, personal computer systems, servercomputer systems, thin clients, thick clients, handheld or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputersystems, mainframe computer systems, and distributed cloud computingenvironments that include any of the above systems or devices, and thelike.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 100, a system memory 106, anda bus 104 that couples various system components including system memory106 to processor 100. The processor 100 may include a program module 102that performs the methods described herein. The module 102 may beprogrammed into the integrated circuits of the processor 100, or loadedfrom memory 106, storage device 108, or network 114 or combinationsthereof.

Bus 104 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 106 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 108 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 104 by one or more data media interfaces.

Computer system may also communicate with one or more external devices116 such as a keyboard, a pointing device, a display 118, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 110.

Still yet, computer system can communicate with one or more networks 114such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 112. Asdepicted, network adapter 112 communicates with the other components ofcomputer system via bus 104. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include anon-transitory computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

In addition, while preferred embodiments of the present invention havebeen described using specific terms, such description is forillustrative purposes only, and it is to be understood that changes andvariations may be made without departing from the spirit or scope of thefollowing claims.

What is claimed is:
 1. A computer implemented method for evaluating nodefulfillment capacity in node order assignment, comprising: receiving acurrent order for node order assignment; retrieving data of each nodefrom a plurality of nodes, the retrieved data of each node comprisingcurrent capacity utilization; determining a probability of backlog on anexpected ship date of each node of the plurality of nodes, theprobability of backlog being based on the retrieved current capacityutilization; determining a capacity utilization cost of each node of theplurality of nodes based on the probability of backlog on the expectedship date; automatically calculating a fulfillment cost of each node ofthe plurality of nodes of the current order based on the capacityutilization cost; identifying one or more nodes from the plurality ofnodes of the current order with the lowest fulfillment cost; andautomatically generating a node order assignment assigning the currentorder to one of the one or more nodes of the plurality of nodes with thelowest fulfillment cost.
 2. The method of claim 1, wherein the retrieveddata of each node further comprising backlog data, capacity of a currentday, and capacity of a future day.
 3. The method of claim 2, furthercomprising automatically calculating an actual capacity utilization onthe expected ship date of each node of the plurality of nodes, theactual capacity utilization being based on the retrieved currentcapacity utilization, the capacity of a current day, the capacity of afuture day, and the backlog data.
 4. The method of claim 3, wherein theactual capacity utilization is calculated by adding the backlog on theexpected ship date divided by the capacity of a current day to thecurrent capacity utilization, when the capacity of a current day isenough to fulfill the current order, and the actual capacity utilizationis calculated by backlog on an expected ship date divided by thecapacity of the expected ship day, the backlog on the expected ship datebeing calculated by adding the result of the current capacityutilization multiplied by the capacity of a current day to the backlog,and subtracting the result of the capacity of each day before theexpected ship date multiplied by the backlog days of the current orderbefore the expected ship date, when the capacity of a current day is notenough to fulfill the current order.
 5. The method of claim 1, whereinthe probability of backlog is calculated by historical data ofbacklogged orders at the current capacity utilization divided byhistorical data of total orders at the current capacity utilization. 6.The method of claim 1, wherein the probability of backlog is furtherbased on hours left in the current day.
 7. The method of claim 1,further comprising determining a number of days of backlog on theexpected ship date of each node of the plurality of nodes, the number ofdays of backlog being based on the retrieved current capacityutilization and wherein the determining the capacity utilization cost isfurther based on the number of days of backlog on the expected ship dateof each node of the plurality of nodes.
 8. A computer system fordetermining node order assignment, comprising: a memory; and a processorconfigured to: receiving a current order for node order assignment;retrieving data of each node from a plurality of nodes, the retrieveddata of each node comprising current capacity utilization; determining aprobability of backlog on an expected ship date of each node of theplurality of nodes, the probability of backlog being based on theretrieved current capacity utilization; determining a capacityutilization cost of each node of the plurality of nodes based on theprobability of backlog on the expected ship date; automaticallycalculating a fulfillment cost of each node of the plurality of nodes ofthe current order based on the capacity utilization cost; identifyingone or more nodes from the plurality of nodes of the current order withthe lowest fulfillment cost; and automatically generating a node orderassignment assigning the current order to one of the one or more nodesof the plurality of nodes with the lowest fulfillment cost.
 9. Thecomputer system of claim 8, wherein the retrieved data furthercomprising backlog data, capacity of a current day, and capacity of afuture day and further comprising automatically calculating an actualcapacity utilization on the expected ship date of each node of theplurality of nodes, the actual capacity utilization being based on theretrieved current capacity utilization, the capacity of a current day,the capacity of a future day, and the backlog data.
 10. The computersystem of claim 9, wherein the actual capacity utilization is calculatedby adding the backlog on the expected ship date divided by the capacityof a current day to the current capacity utilization, when the capacityof a current day is enough to fulfill the current order, and the actualcapacity utilization is calculated by backlog on an expected ship datedivided by the capacity of the expected ship day, the backlog on theexpected ship date being calculated by adding the result of the currentcapacity utilization multiplied by the capacity of a current day to thebacklog, and subtracting the result of the capacity of each day beforethe expected ship date multiplied by the backlog days of the currentorder before the expected ship date, when the capacity of a current dayis not enough to fulfill the current order.
 11. The computer system ofclaim 8, wherein the probability of backlog is calculated by historicaldata of backlogged orders at the current capacity utilization divided byhistorical data of total orders at the current capacity utilization 12.The computer system of claim 8, wherein the probability of backlog isfurther based on hours left in the current day.
 13. The computer systemof claim 8, further comprising determining a number of days of backlogon the expected ship date of each node of the plurality of nodes, thenumber of days of backlog being based on the retrieved current capacityutilization and wherein the determining the capacity utilization cost isfurther based on the number of days of backlog on the expected ship dateof each node of the plurality of nodes.
 14. The computer system of claim8, wherein the capacity of a current day is collected from a planneddaily capacity database, the current capacity utilization is based on anode unit assignment database, the backlog data is collected from abacklog database, the backlog cost is collected from a backlog costdatabase, the labor cost is collected from a labor cost database, andthe predetermined capacity utilization threshold is collected from acapacity utilization target range database.
 15. A non-transitory articleof manufacture tangibly embodying computer readable instructions, whichwhen implemented, cause a computer to perform the steps of a method fordetermining node order assignment, comprising: receiving a current orderfor node order assignment; retrieving data of each node from a pluralityof nodes, the retrieved data of each node comprising current capacityutilization; determining a probability of backlog on an expected shipdate of each node of the plurality of nodes, the probability of backlogbeing based on the retrieved current capacity utilization; determining acapacity utilization cost of each node of the plurality of nodes basedon the probability of backlog on the expected ship date; automaticallycalculating a fulfillment cost of each node of the plurality of nodes ofthe current order based on the capacity utilization cost; identifyingone or more nodes from the plurality of nodes of the current order withthe lowest fulfillment cost; and automatically generating a node orderassignment assigning the current order to one of the one or more nodesof the plurality of nodes with the lowest fulfillment cost.
 16. Anon-transitory article of manufacture of claim 15, wherein the retrieveddata further comprising backlog data, capacity of a current day, andcapacity of a future day and further comprising automaticallycalculating an actual capacity utilization on the expected ship date ofeach node of the plurality of nodes, the actual capacity utilizationbeing based on the retrieved current capacity utilization, the capacityof a current day, the capacity of a future day and the backlog data. 17.A non-transitory article of manufacture of claim 16, wherein the actualcapacity utilization is calculated by adding the backlog on the expectedship date divided by the capacity of a current day to the currentcapacity utilization, when the capacity of a current day is enough tofulfill the current order, and the actual capacity utilization iscalculated by backlog on an expected ship date divided by the capacityof the expected ship day, the backlog on the expected ship date beingcalculated by adding the result of the current capacity utilizationmultiplied by the capacity of a current day to the backlog, andsubtracting the result of the capacity of each day before the expectedship date multiplied by the backlog days of the current order before theexpected ship date, when the capacity of a current day is not enough tofulfill the current order.
 18. A non-transitory article of manufactureof claim 15, wherein the probability of backlog is calculated byhistorical data of backlogged orders at the current capacity utilizationdivided by historical data of total orders at the current capacityutilization.
 19. A non-transitory article of manufacture of claim 15,wherein the probability of backlog is further based on hours left in thecurrent day.
 20. A non-transitory article of manufacture of claim 15,further comprising determining a number of days of backlog on theexpected ship date of each node of the plurality of nodes, the number ofdays of backlog being based on the retrieved current capacityutilization and wherein the determining the capacity utilization cost isfurther based on the number of days of backlog on the expected ship dateof each node of the plurality of nodes.